Model-free approaches to discern non-stationary microstructure noise and time-varying liquidity in high-frequency data
In this paper, we provide non-parametric statistical tools to test stationarity of microstructure noise in general hidden Itô semimartingales, and discuss how to measure liquidity risk using high-frequency financial data. In particular, we investigate the impact of non-stationary microstructure nois...
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Veröffentlicht in: | Journal of econometrics 2017-09, Vol.200 (1), p.79-103 |
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description | In this paper, we provide non-parametric statistical tools to test stationarity of microstructure noise in general hidden Itô semimartingales, and discuss how to measure liquidity risk using high-frequency financial data. In particular, we investigate the impact of non-stationary microstructure noise on some volatility estimators, and design three complementary tests by exploiting edge effects, information aggregation of local estimates and high-frequency asymptotic approximation. The asymptotic distributions of these tests are available under both stationary and non-stationary assumptions, thereby enable us to conservatively control type-I errors and meanwhile ensure the proposed tests enjoy the asymptotically optimal statistical power. Besides, it also enables us to empirically measure aggregate liquidity risks by these test statistics. As byproducts, functional dependence and endogenous microstructure noise are briefly discussed. Simulation with a realistic configuration corroborates our theoretical results, and our empirical study indicates the prevalence of non-stationary microstructure noise in New York Stock Exchange. |
doi_str_mv | 10.1016/j.jeconom.2017.05.015 |
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In particular, we investigate the impact of non-stationary microstructure noise on some volatility estimators, and design three complementary tests by exploiting edge effects, information aggregation of local estimates and high-frequency asymptotic approximation. The asymptotic distributions of these tests are available under both stationary and non-stationary assumptions, thereby enable us to conservatively control type-I errors and meanwhile ensure the proposed tests enjoy the asymptotically optimal statistical power. Besides, it also enables us to empirically measure aggregate liquidity risks by these test statistics. As byproducts, functional dependence and endogenous microstructure noise are briefly discussed. 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Simulation with a realistic configuration corroborates our theoretical results, and our empirical study indicates the prevalence of non-stationary microstructure noise in New York Stock Exchange.</description><subject>Asymptotic methods</subject><subject>Endogenous</subject><subject>High frequency trading</subject><subject>High-frequency tests</subject><subject>Liquidity</subject><subject>Microstructure</subject><subject>Noise</subject><subject>Non-stationarity</subject><subject>Risk</subject><subject>Simulation</subject><subject>Stable central limit theorems</subject><subject>Statistical power</subject><subject>Statistical powers</subject><subject>Stock exchanges</subject><subject>Studies</subject><subject>Volatility</subject><issn>0304-4076</issn><issn>1872-6895</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNqFkF1LwzAUhoMoOKc_QQh43XrSNm13JTL8gok3eh3S5HRLWZMtSQf792bMe6_OxXk_eF5C7hnkDFj9OOQDKmfdmBfAmhx4DoxfkBlrmyKr2wW_JDMoocoqaOprchPCAAC8assZOXw6jdus94hU7nbeSbXBQKOj2gSF3lLrbBaijMZZ6Y90NMq7EP2k4uQxfU1ITqtpNCNmhyQxdk23Zj8ZbeKRGks3Zr05NewntOpItYzyllz1chvw7u_Oyc_ry_fyPVt9vX0sn1eZ4ozHrKr6jpeqrctSY9-ClEXVYctZxRvoZNv0NapOlQtVF6xpNeuB86aQdSU177Uu5-ThnJvIUn2IYnCTt6lSsAUvWMFLBknFz6oTWvDYi503Y0IRDMRpYjGIv4nFaWIBXKSJk-_p7MOEcDDoRVAmMaI2HlUU2pl_En4BvOOKkg</recordid><startdate>20170901</startdate><enddate>20170901</enddate><creator>Chen, Richard Y.</creator><creator>Mykland, Per A.</creator><general>Elsevier B.V</general><general>Elsevier Sequoia S.A</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8BJ</scope><scope>FQK</scope><scope>JBE</scope></search><sort><creationdate>20170901</creationdate><title>Model-free approaches to discern non-stationary microstructure noise and time-varying liquidity in high-frequency data</title><author>Chen, Richard Y. ; Mykland, Per A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c515t-44fb53c8633def80aa24be8514570ba87f6ecbc39c62178d1f05572a64ad5fdd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Asymptotic methods</topic><topic>Endogenous</topic><topic>High frequency trading</topic><topic>High-frequency tests</topic><topic>Liquidity</topic><topic>Microstructure</topic><topic>Noise</topic><topic>Non-stationarity</topic><topic>Risk</topic><topic>Simulation</topic><topic>Stable central limit theorems</topic><topic>Statistical power</topic><topic>Statistical powers</topic><topic>Stock exchanges</topic><topic>Studies</topic><topic>Volatility</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Richard Y.</creatorcontrib><creatorcontrib>Mykland, Per A.</creatorcontrib><collection>CrossRef</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><jtitle>Journal of econometrics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Richard Y.</au><au>Mykland, Per A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Model-free approaches to discern non-stationary microstructure noise and time-varying liquidity in high-frequency data</atitle><jtitle>Journal of econometrics</jtitle><date>2017-09-01</date><risdate>2017</risdate><volume>200</volume><issue>1</issue><spage>79</spage><epage>103</epage><pages>79-103</pages><issn>0304-4076</issn><eissn>1872-6895</eissn><abstract>In this paper, we provide non-parametric statistical tools to test stationarity of microstructure noise in general hidden Itô semimartingales, and discuss how to measure liquidity risk using high-frequency financial data. 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subjects | Asymptotic methods Endogenous High frequency trading High-frequency tests Liquidity Microstructure Noise Non-stationarity Risk Simulation Stable central limit theorems Statistical power Statistical powers Stock exchanges Studies Volatility |
title | Model-free approaches to discern non-stationary microstructure noise and time-varying liquidity in high-frequency data |
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